Recommender Systems and Their Advanced Application
A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".
Deadline for manuscript submissions: 20 July 2024 | Viewed by 17840
Special Issue Editors
Interests: data science; machine learning; recommender system
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Today, the renaissance of artificial intelligence (AI) has attracted huge attention in everyday real life. Recommender systems, as one of the most popular applications of AI, have already become an indispensable means for helping web users to identify the most relevant information/services in the era of information overload. The applications of such systems are multifaceted, including targeted advertising, intelligent financial assistant, and e-commerce, and are bringing immense convenience to people’s daily lives.
This Special Issue solicits the latest and most significant contributions on developing and applying advanced recommender systems. Any novel works on recommender systems and/or their innovative applications are welcome.
Relevant topic areas
This Special Issue invites submissions on all topics of algorithms and theories for recommender systems, including but not limited to:
- Deep neural models for recommender systems
- Shallow neural models for recommender systems
- Neural theories, particularly for recommender systems
- Theoretical analysis of neural models for recommender systems
- Theoretical analysis for recommender systems
- Data characteristics and complexity analysis in recommender systems
- Non-IID (non-independent and identical distribution) theories and practices for recommender systems
- Auto-ML for recommender systems
- Privacy issues in recommender systems
- Recommendations on small data sets
- Complex behavior modeling and analysis for recommender systems
- Psychology-driven user modeling for recommender systems
- Brain-inspired neural models for recommender systems
- Explainable recommender systems
- Adversarial recommender systems
- Multimodal recommender systems
- Rich-context recommender systems
- Heterogeneous relation modeling in recommender systems
- Visualization in recommender systems
- New evaluation metrics and methods for recommender systems
- Case study of recommender systems in real-world applications
Dr. Shoujin Wang
Dr. Qi Zhang
Guest Editors
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.
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Keywords
- recommender systems
- recommendations
- user modeling
- machine learning
- deep learning
Planned Papers
The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.
Title: Movie recommendation model based on probabilistic matrix decomposition using hybrid AdaBoost integration
Author: Li
Highlights: we compare and analyze the performance of the proposed model on MovieLens and FilmTrust datasets. In comparison with the PMF, FCM-PMF, Bagging-BP-PMF, and AdaBoost-SVM-PMF models, several experiments show that the mean absolute error of the proposed model increases by 1.24% and 0.79% compared with Bagging-BP-PMF model on two different datasets, and the root-mean-square error increases by 2.55% and 1.87% respectively.
Title: Movie recommendation model based on probabilistic matrix decomposition using hybrid AdaBoost integration
Author: Li
Highlights: This paper proposes that the PMF model is based on the hybrid AdaBoost method. FCM is used to calculate the similarity of the rating matrix of the user-item , which effectively solves the improvement of rating accuracy.